A
licenseB
qualityC
maintenanceAn MCP server that provides LLMs access to other LLMs
4
33 npm
79
MIT
aimdb-mcp
Officialby aimdb-dev
Related Servers
Alternatives to aimdb-mcp
No user-submitted related servers found.
Related Servers
- FlicenseNot gradedqualityCmaintenanceAn MCP server that provides an AI LLM orchestrator supporting multiple providers (LM Studio, Ollama, OpenAI, generic) plus SQLite-backed memory, kanban, and todo databases for persistent task and knowledge management.-
- AlicenseNot gradedqualityCmaintenanceAn MCP server that enables AI applications to interact with DiceDB databases.5MIT
- AlicenseNot gradedqualityCmaintenanceMCP server for AI-assisted Python debugging using debugpy and Debug Adapter Protocol, enabling AI agents to run tests, set breakpoints, and inspect variables via natural language.8MIT
MCP Rubber Duckofficial
AlicenseAqualityCmaintenanceAn MCP server that bridges multiple LLMs (OpenAI-compatible APIs and CLI coding agents) for collaborative debugging and diverse AI perspectives.12539 npm6MIT- AlicenseNot gradedqualityAmaintenanceThe Multi DB MCP Server is a high-performance implementation of the Database Model Context Protocol designed to revolutionize how AI agents interact with databases. Currently supporting MySQL and PostgreSQL databases.431MIT
TDQS
A3.6/5.0
Scored across 30 tools
Disambiguation4/5
Most tools target clearly distinct runtime or architecture actions, such as get_record vs. drain_record vs. set_record. The proposal tools are mostly well separated, though propose_add_record, propose_modify_fields, and propose_modify_key_variants can overlap for record-definition edits, requiring description-level reading to choose correctly.
Naming Consistency4/5
The set is overwhelmingly snake_case and most names follow a verb_noun pattern like get_record, set_record, and propose_add_task. Minor deviations exist: graph_nodes/graph_edges/graph_topo_order are noun-first, and remove_task/rename_record behave like proposals but lack the propose_ prefix used elsewhere.
Tool Count3/5
With 30 tools, the server is heavy for an agent tool surface and splits attention across runtime inspection, graph analysis, profiling, and architecture proposals. The tools are not redundant, but the count is high enough that selection pressure and prompt load become concerns.
Completeness4/5
The surface covers runtime discovery, reads/writes, schema, metrics, profiling, graph topology, validation, and a broad architecture proposal lifecycle. A few gaps remain, such as no explicit remove_connector or modify_task/binary operation, but agents can likely work around them through adjacent proposals.
Maintenance
ActivityActive
ResponsivenessSlow